comparison limma_voom.xml @ 6:39fa12a6d885 draft

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/limma_voom commit 60cae222b10f43f830936c19298bd723ac47e43d

5:d8a55b5f0de0

<tool id="limma_voom" name="limma" version="3.34.9.1">
    <description>
        Perform differential expression with limma-voom or limma-trend
    </description>

    <requirements>

#end if

-l '$adv.lfc'
-p '$adv.pVal'
-d '$adv.pAdjust'

#if $deMethod.de_select == 'voom':
    #if $deMethod.weightOption:
        -w
    #end if

        </conditional>

        <!-- Gene Annotations -->
        <conditional name="anno">
            <param name="annoOpt" type="select" label="Use Gene Annotations?"
                    help="If you provide an annotation file, annotations will be added to the table(s) of differential expression results to provide descriptions for each gene. See Help section below.">
                <option value="no">No</option>
                <option value="yes">Yes</option>
            </param>
            <when value="yes">
                <param name="geneanno" type="data" format="tabular" label="Gene Annotations"/>

                <option value="BH" selected="true">Benjamini and Hochberg (1995)</option>
                <option value="BY">Benjamini and Yekutieli (2001)</option>
                <option value="holm">Holm (1979)</option>
                <option value="none">None</option>
            </param>
            <param name="normalisationOption" type="select" label="Normalisation Method" help="Default: TMM">
                <option value="TMM" selected="true">TMM</option>
                <option value="RLE">RLE</option>
                <option value="upperquartile">Upperquartile</option>
                <option value="none">None (Don't normalise)</option>

    ========== ======= ======= ======= ======== ======== ========

**Gene Annotations:**
Optional input for gene annotations, this can contain more
information about the genes than just an ID number. The annotations will
be available in the differential expression results table and the optional normalised counts table. The file must contain a header row and have the gene IDs in the first column. The number of rows should match that of the counts files, add NA for any gene IDs with no annotation. The Galaxy tool **annotateMyIDs** can be used to obtain annotations for human, mouse, fly and zebrafish.

Example:

    ==========  ==========  ===================================================
    **GeneID**  **Symbol**  **GeneName**

    * **P-Value Adjustment Method:**
      Change the multiple testing control method, the options are BH(1995) and
      BY(2001) which are both false discovery rate controls. There is also
      Holm(1979) which is a method for family-wise error rate control.

**Normalisation Method:**
The most obvious technical factor that affects the read counts, other than gene expression
levels, is the sequencing depth of each RNA sample. edgeR adjusts any differential expression
analysis for varying sequencing depths as represented by differing library sizes. This is

proportion of the total library size, causing the remaining genes to be under-sampled in that
sample. Unless this RNA composition effect is adjusted for, the remaining genes may falsely
appear to be down-regulated in that sample . The edgeR `calcNormFactors` function normalizes for RNA composition by finding a set of scaling factors for the library sizes that minimize the log-fold changes between the samples for most genes. The default method for computing these scale factors uses a trimmed mean of M values (TMM) between each pair of samples. We call the product of the original library size and the scaling factor the *effective library size*. The effective library size replaces the original library size in all downsteam analyses. TMM is the recommended method for most RNA-Seq data where the majority (more than half) of the genes are believed not differentially expressed between any pair of the samples. You can change the normalisation method under **Advanced Options** above. For more information, see the `calcNormFactors` section in the `edgeR User's Guide`_.

**Robust Settings**
Option to use robust settings with eBayes, used by both liamm-voom and limma-trend. Using robust settings is usually recommended to protect against outlier genes, for more information see the `limma User's Guide`_. This is turned on by default.

**Prior Count:**
If the limma-trend method is used, a count (`prior.count`) is added to all counts to avoid taking a log of zero, and damp down the variances of logarithms of low counts. A default of 3 is used, as recommended in the `limma User's Guide`_.

**Apply Sample Weights:**

.. _limma: http://www.bioconductor.org/packages/release/bioc/html/limma.html
.. _limma approach: https://www.ncbi.nlm.nih.gov/pubmed/25605792
.. _limma User's Guide: http://bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf
.. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html
.. _edgeR User's Guide: https://bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf
    ]]></help>
    <citations>
        <citation type="doi">10.1186/gb-2014-15-2-r29</citation>
        <citation type="doi">10.1093/nar/gkv412</citation>
    </citations>

6:39fa12a6d885

<tool id="limma_voom" name="limma" version="3.34.9.2">
    <description>
        Perform differential expression with limma-voom or limma-trend
    </description>

    <requirements>

#end if

-l '$adv.lfc'
-p '$adv.pVal'
-d '$adv.pAdjust'
-v '$adv.volgenes'
#if $adv.treat:
    -T
#end if

#if $deMethod.de_select == 'voom':
    #if $deMethod.weightOption:
        -w
    #end if

        </conditional>

        <!-- Gene Annotations -->
        <conditional name="anno">
            <param name="annoOpt" type="select" label="Use Gene Annotations?"
                    help="If you provide an annotation file, annotations will be added to the table(s) of differential expression results to provide descriptions for each gene, and used to label the top genes in the Volcano plot. See Help section below.">
                <option value="no">No</option>
                <option value="yes">Yes</option>
            </param>
            <when value="yes">
                <param name="geneanno" type="data" format="tabular" label="Gene Annotations"/>

                <option value="BH" selected="true">Benjamini and Hochberg (1995)</option>
                <option value="BY">Benjamini and Yekutieli (2001)</option>
                <option value="holm">Holm (1979)</option>
                <option value="none">None</option>
            </param>
            <param name="treat" type="boolean" truevalue="1" falsevalue="0" checked="False"
                label="Test significance relative to a fold-change threshold (TREAT)"
                help="If you want to apply a cut-off on a fold change the TREAT function can be used, see Help section below. Default: No"/>
            <param name="volgenes" type="integer" value="10" min="0"
                label="Number of genes to highlight in Volcano plot"
                help="The top DE genes will be highlighted in the Volcano plot for each contrast. Default: 10."/>
            <param name="normalisationOption" type="select" label="Normalisation Method" help="Default: TMM">
                <option value="TMM" selected="true">TMM</option>
                <option value="RLE">RLE</option>
                <option value="upperquartile">Upperquartile</option>
                <option value="none">None (Don't normalise)</option>

    ========== ======= ======= ======= ======== ======== ========

**Gene Annotations:**
Optional input for gene annotations, this can contain more
information about the genes than just an ID number. The annotations will
be available in the differential expression results table and the optional normalised counts table. The file must contain a header row and have the gene IDs in the first column. The second column will be used to label the genes in the Volcano plot instead of the default Gene IDs. The number of rows should match that of the counts files, add NA for any gene IDs with no annotation. The Galaxy tool **annotateMyIDs** can be used to obtain annotations for human, mouse, fly and zebrafish.

Example:

    ==========  ==========  ===================================================
    **GeneID**  **Symbol**  **GeneName**

    * **P-Value Adjustment Method:**
      Change the multiple testing control method, the options are BH(1995) and
      BY(2001) which are both false discovery rate controls. There is also
      Holm(1979) which is a method for family-wise error rate control.

**Testing relative to a threshold (TREAT):**
If there are a lot of differentially expressed genes, a fold change threshold can be applied in addition to the P-value threshold to select genes that are more likely to be biologically significant. However, ranking by P-value and discarding genes with small logFCs can increase the false discovery rate. Using the limma TREAT function performs this analysis correctly (`McCarthy and Smyth, 2009`_).

**Normalisation Method:**
The most obvious technical factor that affects the read counts, other than gene expression
levels, is the sequencing depth of each RNA sample. edgeR adjusts any differential expression
analysis for varying sequencing depths as represented by differing library sizes. This is

proportion of the total library size, causing the remaining genes to be under-sampled in that
sample. Unless this RNA composition effect is adjusted for, the remaining genes may falsely
appear to be down-regulated in that sample . The edgeR `calcNormFactors` function normalizes for RNA composition by finding a set of scaling factors for the library sizes that minimize the log-fold changes between the samples for most genes. The default method for computing these scale factors uses a trimmed mean of M values (TMM) between each pair of samples. We call the product of the original library size and the scaling factor the *effective library size*. The effective library size replaces the original library size in all downsteam analyses. TMM is the recommended method for most RNA-Seq data where the majority (more than half) of the genes are believed not differentially expressed between any pair of the samples. You can change the normalisation method under **Advanced Options** above. For more information, see the `calcNormFactors` section in the `edgeR User's Guide`_.

**Robust Settings**
Option to use robust settings with eBayes or TREAT, used by both limma-voom and limma-trend. Using robust settings is usually recommended to protect against outlier genes, for more information see the `limma User's Guide`_ and `Phipson et al. 2016`_. This is turned on by default.

**Prior Count:**
If the limma-trend method is used, a count (`prior.count`) is added to all counts to avoid taking a log of zero, and damp down the variances of logarithms of low counts. A default of 3 is used, as recommended in the `limma User's Guide`_.

**Apply Sample Weights:**

.. _limma: http://www.bioconductor.org/packages/release/bioc/html/limma.html
.. _limma approach: https://www.ncbi.nlm.nih.gov/pubmed/25605792
.. _limma User's Guide: http://bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf
.. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html
.. _edgeR User's Guide: https://bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf
.. _McCarthy and Smyth, 2009: https://www.ncbi.nlm.nih.gov/pubmed/19176553
.. _Phipson et al. 2016: https://www.ncbi.nlm.nih.gov/pubmed/28367255
    ]]></help>
    <citations>
        <citation type="doi">10.1186/gb-2014-15-2-r29</citation>
        <citation type="doi">10.1093/nar/gkv412</citation>
    </citations>